News Item Extraction for Text Mining inWeb Newspapers

机译：Web报纸中用于文本挖掘的新闻项提取

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Web newspapers provide a valuable resource for information. In order to benefit more from the available information, text mining techniques can be applied. However, because each newspaper page often covers a lot of unrelated topics, page-based data mining will not always give useful results. In order to improve on complete-page mining, we present an approach based on extracting the individual news items from the web pages and mining these separately. Automatic news item extraction is a difficult problem, and in this paper we also provide strategies solving that task. We study the quality of the news item extraction, and also provide results from clustering the extracted news items.

机译：网络报纸提供了宝贵的信息资源。为了从可用信息中受益更多，可以应用文本挖掘技术。但是，由于每个报纸的页面通常包含很多不相关的主题，因此基于页面的数据挖掘将不会总是提供有用的结果。为了改进完整页面的挖掘，我们提出了一种基于从网页中提取单个新闻项并分别对其进行挖掘的方法。新闻自动提取是一个困难的问题，在本文中，我们还提供了解决该任务的策略。我们研究了新闻项提取的质量，并提供了对提取的新闻项进行聚类的结果。

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《》|2005年|P.195-204|共10页
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Norvag; k.; Oyri; R.;
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News Item Extraction for Text Mining inWeb Newspapers

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